IEEE Symposium on

Computational Intelligence and Ensemble Learning

Ensemble learning
attempts to enhance the performance of systems (clustering,
classification, prediction, feature selection, search, optimization,
rule extraction, etc.) by using multiple models instead of using a
single model. This approach is intuitively meaningful as a single model
may not always be the best for solving a complex problem (also known as
the no free lunch theorem) while multiple models are more likely to
yield results better than each of the constituent models. Although in
the past, ensemble methods have been mainly studied in the context of
classification and time series prediction, recently they are being used
in algorithms in other scenarios such as clustering, fuzzy systems,
evolutionary algorithms, dimensionality reduction and so on.

The manuscripts should be submitted in PDF format. Click Here to know further guidelines for submission.

The
aim of this symposium is to bring together researchers and
practitioners who are working in the overlapping fields of ensemble
methods and computational intelligence. Papers dealing with theory,
algorithms, analysis, and applications of ensemble of computational
intelligence methods are sought for this symposium.